fashionpedia

# Dataset Card for Fashionpedia

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://fashionpedia.github.io/home/index.html
- **Repository:** https://github.com/cvdfoundation/fashionpedia
- **Paper:** https://arxiv.org/abs/2004.12276

### Dataset Summary

Fashionpedia is a dataset mapping out the visual aspects of the fashion world.
From the paper:
> Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology.
Fashionpedia has:
- 46781 images
- 342182 bounding-boxes

### Supported Tasks

- Object detection
- Image classification

### Languages

All of annotations use English as primary language.

## Dataset Structure

The dataset is structured as follows:
```py
DatasetDict({
train: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 45623
})
val: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 1158
})
})
```

### Data Instances

An example of the data for one image is:
```py
{'image_id': 23,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=682x1024>,
'width': 682,
'height': 1024,
'objects': {'bbox_id': [150311, 150312, 150313, 150314],
'category': [23, 23, 33, 10],
'bbox': [[445.0, 910.0, 505.0, 983.0],
[239.0, 940.0, 284.0, 994.0],
[298.0, 282.0, 386.0, 352.0],
[210.0, 282.0, 448.0, 665.0]],
'area': [1422, 843, 373, 56375]}}
```

With the type of each field being defined as:
```py
{'image_id': Value(dtype='int64'),
'image': Image(decode=True),
'width': Value(dtype='int64'),
'height': Value(dtype='int64'),
'objects': Sequence(feature={
'bbox_id': Value(dtype='int64'),
'category': ClassLabel(num_classes=46, names=['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']),
'bbox': Sequence(feature=Value(dtype='float64'), length=4),
'area': Value(dtype='int64')},
length=-1)}
```

### Data Fields

The dataset has the following fields:

- `image_id`: Unique numeric ID of the image.
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: Image width.
- `height`: Image height.
- `objects`: A dictionary containing bounding box metadata for the objects in the image:
- `bbox_id`: Unique numeric ID of the bounding box annotation.
- `category`: The object’s category.
- `area`: The area of the bounding box.
- `bbox`: The object’s bounding box (in the Pascal VOC format)

### Data Splits

| | Train | Validation | Test |
|----------------|--------|------------|------|
| Images | 45623 | 1158 | 0 |
| Bounding boxes | 333401 | 8781 | 0 |

## Additional Information

### Licensing Information

Fashionpedia is licensed under a Creative Commons Attribution 4.0 International License.

### Citation Information

```
@inproceedings{jia2020fashionpedia,
title={Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset},
author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge}
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}
```

### Contributions

Thanks to [@blinjrm](https://github.com/blinjrm) for adding this dataset.

# Fashionpedia 数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集摘要](#dataset-summary)
- [支持任务与基准排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [附加信息](#additional-information)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献说明](#contributions)

## 数据集描述
- **主页**：https://fashionpedia.github.io/home/index.html
- **代码仓库**：https://github.com/cvdfoundation/fashionpedia
- **论文**：https://arxiv.org/abs/2004.12276

### 数据集摘要
Fashionpedia 是覆盖时尚领域视觉元素的数据集。根据原论文所述：
> Fashionpedia 是一个全新的数据集，包含两个组成部分：(1) 由时尚专家构建的本体，涵盖27个主流服饰类别、19个服饰部件、294个细粒度属性及其关联关系；(2) 基于Fashionpedia本体构建的日常与名人活动时尚图像数据集，带有分割掩码（segmentation mask）以及每个掩码对应的细粒度属性标注。
Fashionpedia 包含：
- 46781张图像
- 342182个边界框（bounding box）

### 支持任务
- 目标检测
- 图像分类

### 语言
所有标注均以英语作为主要语言。

## 数据集结构
数据集结构如下：
py
DatasetDict({
train: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 45623
})
val: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 1158
})
})

### 数据实例
单张图像的数据示例如下：
py
{'image_id': 23,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode="RGB" size=682x1024>,
'width': 682,
'height': 1024,
'objects': {'bbox_id': [150311, 150312, 150313, 150314],
'category': [23, 23, 33, 10],
'bbox': [[445.0, 910.0, 505.0, 983.0],
[239.0, 940.0, 284.0, 994.0],
[298.0, 282.0, 386.0, 352.0],
[210.0, 282.0, 448.0, 665.0]],
'area': [1422, 843, 373, 56375]}}

各字段的类型定义如下：
py
{'image_id': Value(dtype='int64'),
'image': Image(decode=True),
'width': Value(dtype='int64'),
'height': Value(dtype='int64'),
'objects': Sequence(feature={
'bbox_id': Value(dtype='int64'),
'category': ClassLabel(num_classes=46, names=['衬衫、上衣', 'T恤、卫衣', '毛衣', '开衫', '夹克', '背心', '长裤', '短裤', '半身裙', '外套', '连衣裙', '连体衣', '斗篷', '眼镜', '帽子', '发带/头饰', '领带', '手套', '手表', '腰带', '腿套', '连裤袜/长筒袜', '短袜', '鞋履', '包袋/钱包', '围巾', '雨伞', '兜帽', '衣领', '翻领', '肩章', '衣袖', '口袋', '领口', '搭扣', '拉链', '贴花', '串珠', '蝴蝶结', '花朵', '流苏', '饰带', '铆钉', '荷叶边', '亮片', '穗带']),
'bbox': Sequence(feature=Value(dtype='float64'), length=4),
'area': Value(dtype='int64')},
length=-1)}

### 数据字段
本数据集包含以下字段：
- `image_id`：图像的唯一数字标识符。
- `image`：包含图像的`PIL.Image.Image`对象。请注意，当访问图像列时：`dataset[0]["image"]` 会自动对图像文件进行解码。解码大量图像文件可能会耗费大量时间，因此建议优先通过样本索引查询，即**始终优先使用`dataset[0]["image"]`而非`dataset["image"][0]`**
- `width`：图像宽度。
- `height`：图像高度。
- `objects`：包含图像中目标边界框元数据的字典：
- `bbox_id`：边界框标注的唯一数字标识符。
- `category`：目标的类别。
- `area`：边界框的面积。
- `bbox`：目标的边界框（采用Pascal VOC格式）

### 数据划分
| | 训练集 | 验证集 | 测试集 |
|----------------|--------|------------|------|
| 图像数量 | 45623 | 1158 | 0 |
| 边界框数量 | 333401 | 8781 | 0 |

## 附加信息
### 许可信息
Fashionpedia 采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International License）。

### 引用信息

@inproceedings{jia2020fashionpedia,
title={Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset},
author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge}
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}

### 贡献说明
感谢 [@blinjrm](https://github.com/blinjrm) 为本数据集添加相关内容。